tensorflow练习-fashion-mnist-保存模型，导入模型

训练模型，在保存模型，使得下次调用不用再次训练参数

from tensorflow import keras
import tensorflow as tf
import tensorflow.python.keras

fashion_mnist=keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()

class_name=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Sneaker','Shirt','Bag','Ankle boot']


model = keras.Sequential()  # 构建序列模型
model.add(keras.layers.Flatten(input_shape=(28,28)))#将数据拉长
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(train_images,train_labels, epochs=10)

test_loss,test_acc=model.evaluate(test_images,test_labels,verbose=2)
print('\nTest accuracy:',test_acc)

predictions=model.predict(test_images)
predictions.shape
print(predictions[0])


#保存好训练好的模型，保存权重参数和网络模型，通常以.h5为后缀名,以后使用就不用训练模型了
model.save('fashion_model.h5')

调用保存的模型

import tensorflow as tf
from tensorflow.python.keras import layers
from tensorflow import keras
model=keras.models.load_model('fashion_model.h5')

fashion_mnist=keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()
predictions=model.predict(test_images)
print(predictions.shape)
print(predictions[0])